This paper investigates a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at the jump times of a homogeneous Poisson process. Dr…
In this paper, we study Lichnerowicz type estimate for eigenvalues of drifting Laplacian operator and L1 and L2 energy for drifting heat equation on closed manifolds with weighted measure. In some sense, this study is about the eigenvalue estimate on Ricci solitons.
In this paper, we investigate eigenvalues of the Dirichlet problem and the closed eigenvalue problem of drifting Laplacian on the complete metric measure spaces and establish the corresponding general formulas. By using those general formulas, we give some upper bounds of consecutive gap of the eigenvalues of the eigen…
Unified techniques improve stability and replicability in changing data.
problem Concept drift in data generating distribution.
method Removing hidden confounding and causal regularization.
result Improves stability, replicability, and robustness in heterogeneous data.
Optimal B-robust estimate is constructed for multidimensional parameter in drift coefficient of diffusion type process with small noise. Optimal mean-variance robust (optimal V -robust) trading strategy is find to hedge in mean-variance sense the contingent claim in incomplete financial market with arbitrary informatio…
Improved embeddings by topic-sensitive attention on large corpora.
problem Capturing sense of words in limited corpora using pretrained embeddings.
method Topic-sensitive attention on large topic-rich corpora to correct sense drift in pretrained embeddings.
result Limited corpus augmentation is more effective than adapting pretrained embeddings.
Simulates risk-neutral markets using neural spline flows.
problem Creating realistic risk-neutral market simulations.
method Developed a low-dimensional martingale representation and used neural spline flows for sampling.
result The calibrated simulator is closest to historical data with respect to Kullback-Leibler divergence.
Study extends wealth tax neutrality framework to heterogeneous investors.
problem Analyzing wealth tax neutrality in populations with varying return-generating ability.
method Extended Fokker-Planck framework to heterogeneous investors, deriving extended Fokker-Planck equation.
result Proportional wealth tax no longer neutral due to varying return-generating ability, leading to different real incidence and wealth distribution changes.
Study approximates worst-case stock trading under uncertainty, quantifying sensitivity.
problem Maximizing worst-case cost of stock gains and losses under uncertainty.
method Approximates worst-case problem by baseline problem as uncertainty vanishes.
result Value of worst-case problem equals baseline value plus correction term.
We study robust notions of good-deal hedging and valuation under combined uncertainty about the drifts and volatilities of asset prices. Good-deal bounds are determined by a subset of risk-neutral pricing measures such that not only opportunities for arbitrage are excluded but also deals that are too good, by restricti…
This review covers learning under concept drift, including detection, understanding, and adaptation.
problem Unforeseeable changes in data distribution over time impact machine learning performance.
method Reviews and analyzes methodologies and techniques for concept drift detection, understanding, and adaptation.
result Establishes a framework for learning under concept drift with three main components.
New framework for detecting data drift in continuous time.
problem Drift in data distribution over time.
method Probability theoretical framework for continuous time drift.
result New efficient drift detection method and decomposition of data.
Identifies features most relevant to concept drift in data.
problem Identifying features most relevant to concept drift.
method Distinguishing between drift inducing and faithfully drifting features; deriving minimal subsets of features to characterize drift.
result Derives a detection algorithm for concept drift.
New method detects when models influence their own drift in real-time data streams.
problem Models can induce concept drift in real-time data streams.
method CheckerBoard Performative Drift Detection (CB-PDD)
result CB-PDD effectively detects performative drift in real-time data streams.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
problem The challenge of detecting data distribution changes (drift) in real-time systems.
method Developed adversarial data streams to show weaknesses in existing drift detection schemes.
result Demonstrated that common drift detection methods can be fooled by adversarial data streams.
Paper proposes a semi-supervised method for detecting concept drift in streaming environments.
problem Detecting concept drift in streaming environments with limited labeled data.
method Utilizes density estimation of posterior probabilities in partially labeled streaming data.
result Demonstrates superior concept drift detection in streaming environments with limited labeled data.
A new drift detection method based on autoregressive models.
problem Concept drift in real-world data leads to decreased model performance.
method Autoregressive based drift detection method (ADDM).
result ADDM outperforms state-of-the-art drift detection methods.
Adaptive sampling detects local concept drift with limited labels.
problem Detecting local concept drift in dynamic environments with scarce labels.
method Combines residual-based exploration and exploitation with EWMA monitoring.
result Superior performance in label efficiency and drift detection accuracy.
Algorithm detects concept drift and adapts models in streaming data.
problem Concept drift in streaming data renders models inaccurate.
method Adaptive learning algorithm that detects drifts and reacts to them.
result Risk competitive to an algorithm with perfect drift knowledge.
Classifiers operating in a dynamic, real world environment, are vulnerable to adversarial activity, which causes the data distribution to change over time. These changes are traditionally referred to as concept drift, and several approaches have been developed in literature to deal with the problem of drift handling an…
Classifies polynomial growth solutions to drift-harmonic equations on asymptotically paraboloidal manifolds.
problem Classifying polynomial growth solutions to drift-harmonic equations on specific types of manifolds.
method Inductive argument that alternates between constructing and asymptotically controlling drift-harmonic functions.
result All drift-harmonic functions with polynomial growth asymptotically separate variables and dimensions of spaces are computed.
New method detects drift in high-dimensional data.
problem Understanding and localizing concept drift in learning systems.
method Conformal predictions for drift localization.
result Our approach outperforms existing methods on image datasets.
Detects drifts in data for classification tasks using constrained embeddings.
problem Drifts in data affect model performance; unsupervised methods ignore label information.
method Task-sensitive semi-supervised drift detection with constrained low-dimensional embedding.
result Successfully detects real drifts affecting classification performance.
PDD detects concept drift using explainable AI, improving model performance in dynamic environments.
problem Detecting and adapting to concept drift in predictive models.
method Profile Drift Detection (PDD) using Partial Dependence Profiles (PDPs).
result PDD outperforms existing methods in detecting concept drift and maintaining high predictive performance.
This paper studies concept drift detectors for financial time series.
problem Improving accuracy on financial time series with concept drifts.
method Three simple concept drift detectors tailored to financial time series.
result Two of the detectors are as effective as state-of-the-art detectors.
Paper proposes a framework to detect adversarial concept drifts under poisoning attacks.
problem Adversarial concept drift in data streams.
method Augmented Restricted Boltzmann Machine with improved gradient computation and energy function.
result High robustness and efficacy of the proposed drift detection framework in adversarial scenarios.
Paper presents IMRCs for evolving tasks with forward and backward learning.
problem Incremental learning of evolving tasks with few samples per task.
method Incremental minimax risk classifiers (IMRCs) that exploit forward and backward learning.
result IMRCs provide significant performance improvement, especially with reduced sample sizes.
Kernel-Gradient Drifting improves generative modeling for non-Euclidean data.
problem Challenges in generative modeling for non-Euclidean data.
method Replaces Euclidean displacement with kernel-induced directions, exposing score-based structure.
result Kernel-gradient drifting enables state-of-the-art one-step generation for non-Euclidean data.
A framework for evaluating and benchmarking concept drift detection methods
problem Data stream mining challenged by concept drift
method A novel benchmarking framework
result Fair comparisons of drift detection methods
A new method detects concept drift in streaming data using k-means space partitioning.
problem Detecting distribution changes in streaming data.
method Equal intensity k-means space partitioning (EI-kMeans) and heuristic sensitivity improvement.
result EI-kMeans improves drift detection accuracy and sensitivity.
Visual analytics tool detects and corrects concept drift in data streams.
problem Concept drift causes inaccurate predictions in evolving data.
method DriftVis combines drift detection and visualization.
result Visual analytics supports detection, examination, and correction of concept drift.
CURIE uses cellular automata to detect concept drift in data streams.
problem Detecting changes in data distribution (concept drift) in data streams.
method CURIE represents data stream distribution in a cellular automata grid and uses its neighborhood rule to detect changes.
result CURIE, when hybridized with base learners, performs competitively in detection metrics and classification accuracy.
Mean-field neural nets approximate functions using a free energy functional and controlled dynamics.
problem Function approximation by two-layer neural nets in the mean-field regime.
method Phrasing function approximation as global minimization of a free energy functional, examining dynamics in the space of probability measures over weights.
result Characterization of the unique global minimizer and dynamics achieving it, including the Föllmer drift.
Gradient-based methods for games suffer from discrete update steps that cause drift, affecting performance.
problem Gradient-based methods for two-player games suffer from drift due to discrete update steps.
method Derived modified continuous dynamical systems to closely follow the discrete dynamics of games.
result Identified distinct components of discretization drift that can alter or destabilize game performance.
Paper introduces a method to explain concept drift using counterfactual explanations.
problem Understanding the features where concept drift occurs for better model adjustment.
method Formal definition and algorithm based on counterfactual explanations.
result Demonstrates usefulness of the method in various examples.
Estimates drift functions in SDEs using denoising diffusion models.
problem Estimating time-homogeneous drift functions in multivariate SDEs.
method Formulates drift estimation as a denoising problem, trains a conditional diffusion model.
result Proposed estimator matches classical methods in low dimensions and remains competitive in higher dimensions.
Paper proposes a method to automatically detect drift in machine learning models.
problem Detecting changes in class-label data distributions that affect model predictions.
method Self-evaluating predictive model degradation to detect concept drift.
result Effectiveness in automatically detecting and describing concept drift.
A new approach switches between simple and complex models to handle concept drifts in regression tasks.
problem Handling concept drifts in regression models to maintain accurate predictions over time.
method Error Intersection Approach: switches between simple and complex models based on drift detection.
result The Error Intersection Approach significantly outperforms baselines in handling concept drifts in a real-world taxi demand dataset.
A drift detection method for large datasets without labels.
problem Early detection of concept drift in large, unlabeled datasets.
method Classical statistical process control in a label-less setting.
result Better statistical power than previous methods under computational constraints.
Adversarial validation detects concept drift in user targeting systems.
problem Concept drift in user targeting automation systems deteriorates model performance over time.
method Adversarial validation approach to detect and adapt to concept drift.
result Adversarial validation effectively addresses concept drift in user targeting systems.
Paper benchmarks machine learning for detecting process curve drifts.
problem Detecting drifts in multivariate manufacturing process data.
method Synthetic data generation and evaluation score introduction.
result Existing algorithms often fail with complex drift scenarios.
Approach to detect and adapt to concept drift in unlabeled streaming data.
problem Detect and adapt to concept drift in high-dimensional, noisy, low-context data.
method Density-based clustering for virtual drift and weak supervision for real drift.
result 90% precision in detecting and adapting to concept drift for 4 years after initial deployment.
The paper optimizes portfolios in a market with hidden drift and random expert opinions.
problem Optimizing portfolios in a market with hidden Gaussian drift and random expert signals.
method Modeling the hidden drift using Kalman filters and solving the utility maximization problem with dynamic programming.
result Derivation of optimal portfolio weights and utility maximization under the given market conditions.
Study non-stationary distributions, proving risk bounds for density estimation.
problem Estimating current distribution under gradual changes.
method Proves tight minimax risk bounds for nonparametric density estimation under drift.
result Generalizes previous results on agnostic learning under drift.
A new method detects concept drift without true labels.
problem Detecting concept drift in unsupervised settings.
method Student-teacher learning paradigm for drift detection.
result The method outperforms state-of-the-art approaches in experiments.
Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to d…
A new framework detects concept drift in streaming data.
problem Detecting distributional changes in non-stationary data streams.
method Treating model parameters as random variables, ERICS uses information theory measures to identify concept drift.
result ERICS effectively detects concept drift compared to existing methods.
Framework monitors insurance pricing models for drift and recalibration.
problem Maintaining predictive performance of pricing models in evolving insurance portfolios.
method Formalizes deviance loss and Murphy's score, studies Gini score, develops monitoring framework.
result Framework guides decisions on refitting or recalibrating pricing models.